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Refining the Long-Short

Refining the Long-Short. Matthew J. Morse Term 4, 2005. Where we left off…. In GAA, I pursued a sector-specific long-short strategy: Targeted market-neutrality Industry-specific long and short “baskets” were identified on a monthly basis through a screen of targeted factors

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Refining the Long-Short

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  1. Refining the Long-Short Matthew J. Morse Term 4, 2005

  2. Where we left off… • In GAA, I pursued a sector-specific long-short strategy: • Targeted market-neutrality • Industry-specific long and short “baskets” were identified on a monthly basis through a screen of targeted factors • Results were mixed • Factors provided attractive In-Sample results but were less compelling Out-of-Sample • While less volatile over-all and market neutral, the strategy did not outperform the benchmark on an absolute basis

  3. Next Steps…#1,#2,#3 • Model Improvement: • 1) Fix the Factors: Test additional screening criteria to develop a “better” model • 2) Optimize: First-cut model employed subjective scoring for multivariate screens. Optimization provides a more robust approach to weighting factors • 3)Dynamic Weighting: Explore the benefits of time-varying factor weights to incorporate information from macroeconomic events

  4. Next Steps…#4 • Tailor the Implementation: • 4) Make the strategy deployable at a small scale: • Not an asset manager by trade, so adjust application so I can use this knowledge for personal fun and profit • Limit trading: Valuation Screen as a fundamental overlay to reduce number of securities traded • Less Rebalancing: Extend rebalancing period and assess impact to returns

  5. #1 – Fix the Factors • Original: • Short Interest - total short positions currently open for a given equity as a percentage of total shares • Fundamental Debt Factor - incorporates information about cost of debt and leverage ratio • Change in Consensus – Percent change in EPS estimate • Revised: • Prospective Earnings Yield – provides a primary fundamental measure and is expectational in nature • 3 month revision rate – indicates direction of most recent information observe by market analysts • 36 mo MA – 60 mo MA – technical factor to indicate momentum and trends Outcome: Better Screen, Better Results

  6. 3 month Revision Alpha

  7. 36 mo Moving Avg – 60 mo Moving Avg Alpha

  8. Earnings Yield Alpha

  9. #2 – Optimize • Use optimization (as opposed to subjective scoring to build multi-variant screen • Target different acceptable level of volatility (sensitivity analysis)

  10. #2 – Optimized Heat Map Alpha Outcome: Better Screen, Better Results

  11. #2 – Optimize Excel Printouts: Comparison on frontier

  12. #3 – Dynamic Factors Strategy Returns in Periods with Flattening Yield Curves • Interacting with Yield Curve Slope • Changes in Weighting when 10 yr yield less 6 mo yield is less than 1% • New Optimization delivers different weightings • Minor change in performance • 1st Episode = 0.045%/month advantage • 2nd Episode = (0.017%)/month • Not Compelling as Executed Nov ’88 – Jun ‘90 Nov ’97 – Feb ‘01 Interacted for Dynamic weighting Static Weighting

  13. #4 – Scale Down • Implementation is Difficult at small scale: • With scoring screen limiting to 250 securities, still long 50 and short 50 • Monthly rebalancing prohibitive • Solution: • Automate a fundament sort of the long-short basket to further reduce holdings • Explore rebalance at 6 / 12 month intervals

  14. #4 – Valuation Screen • Goal: apply an objective criteria to narrow the total population of the identified long and short baskets • Process: • Attain consensus forecasts for stocks in long and short basket • Apply mechanical valuation, building and explicit valuation forecast for each security in in the 1st and 5th fractile • Assume consensus performance and a 15 yr linear ramp-down to performance at their cost of capital • Compare mechanical valuation to market price– select top 10 undervalued “longs” and top 10 overvalued “shorts” • Results: • Painful to apply (manual data entry, requires a more elegant execution) • Potential for information gaps (i.e. lack of forecast, not all securities covered) • Sacrifices returns (on average), spikes volatility Disclaimer: more rigorous back-testing necessary

  15. #4 – Valuation Screen Excel Printouts: Structure of inputs and sort on valuation information

  16. #4 – Rebalance Period • Goal: assess trade-off in expected return from longer rebalancing Return SD Sharpe 1 month: 8.5% .11 .78 6 month: 7.0% .08 .85 12 month: 8.7% .15 .60 Conclusion: Small sacrifice for longer rebalance

  17. Conclusions • Optimization beats Subjective • Dynamic Factoring must be refined • Long-Short can be scaled down, but risk obviously increases • A little bit of knowledge can be dangerous

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